Data Management in Precision Oncology at OHSU

Patrick Leyshock Ph.D., Senior Manager of the Data Management System Operations Team, Knight Cancer Center



The Knight’s Precision Oncology program depends on a complete, correct, and integrated collection of clinical and research data. In collaboration with other groups at the University, Leyshock’s team designs, implements, and maintains both technologies and work processes for this collection. This presentation will focus on the Knight Cancer Institute’s LabKey-based precision oncology data management system and will cover multiple aspects of the system, including its architecture, workflows, and user types.


Overcoming Data Management Challenges

At the Knight Cancer Institute, the traditional data management practices were not conducive to the needs of precision oncology. Data was often siloed, with researchers and clinicians navigating through multiple platforms to gather patient-specific information. The introduction of LabKey Server by the team marked a big change, offering a unified platform that integrates diverse data types essential for precision oncology.

Server SDMS: The Core of Data Integration

Server SDMS functions as the heart of the Knight Cancer Institute’s data management system, integrating data from various sources into a coherent, accessible format. This integration is crucial for developing a comprehensive understanding of each patient’s tumor, facilitating both clinical decision-making and research. Through features like patient-centric and sample-centric views, Server SDMS presents data in an intuitive manner, making it easier for clinicians and researchers to derive actionable insights.

Innovations and Integrations

The Knight Cancer Institute has leveraged Server SDMS to create innovative solutions for data visualization and management. Integrations with platforms like Tableau for data reporting and OMERO for image management exemplify the system’s flexibility and capability to address specific research and clinical needs. These integrations not only enhance the usability of the data but also streamline workflows, reducing the time and effort required to access and analyze critical information.

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